Package org.apache.spark.sql.streaming

Class DataStreamReader

Object
org.apache.spark.sql.streaming.DataStreamReader
All Implemented Interfaces:
org.apache.spark.internal.Logging
public final class DataStreamReader extends Object implements org.apache.spark.internal.Logging
Interface used to load a streaming Dataset from external storage systems (e.g. file systems, key-value stores, etc). Use SparkSession.readStream to access this.

Since:
2.0.0

  • Nested Class Summary

    Nested classes/interfaces inherited from interface org.apache.spark.internal.Logging

    org.apache.spark.internal.Logging.LogStringContext, org.apache.spark.internal.Logging.SparkShellLoggingFilter

  • Method Summary

    Modifier and Type
    Method
    Description
    Dataset<Row>
    csv(String path)
    Loads a CSV file stream and returns the result as a DataFrame.
    DataStreamReader
    format(String source)
    Specifies the input data source format.
    Dataset<Row>
    json(String path)
    Loads a JSON file stream and returns the results as a DataFrame.
    Dataset<Row>
    load()
    Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g.
    Dataset<Row>
    load(String path)
    Loads input in as a DataFrame, for data streams that read from some path.
    DataStreamReader
    option(String key, boolean value)
    Adds an input option for the underlying data source.
    DataStreamReader
    option(String key, double value)
    Adds an input option for the underlying data source.
    DataStreamReader
    option(String key, long value)
    Adds an input option for the underlying data source.
    DataStreamReader
    option(String key, String value)
    Adds an input option for the underlying data source.
    DataStreamReader
    options(Map<String,String> options)
    (Java-specific) Adds input options for the underlying data source.
    DataStreamReader
    options(scala.collection.Map<String,String> options)
    (Scala-specific) Adds input options for the underlying data source.
    Dataset<Row>
    orc(String path)
    Loads a ORC file stream, returning the result as a DataFrame.
    Dataset<Row>
    parquet(String path)
    Loads a Parquet file stream, returning the result as a DataFrame.
    DataStreamReader
    schema(String schemaString)
    Specifies the schema by using the input DDL-formatted string.
    DataStreamReader
    schema(StructType schema)
    Specifies the input schema.
    Dataset<Row>
    table(String tableName)
    Define a Streaming DataFrame on a Table.
    Dataset<Row>
    text(String path)
    Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any.
    Dataset<String>
    textFile(String path)
    Loads text file(s) and returns a Dataset of String.
    Dataset<Row>
    xml(String path)
    Loads a XML file stream and returns the result as a DataFrame.

    Methods inherited from class java.lang.Object

    equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.apache.spark.internal.Logging

    initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, isTraceEnabled, log, logDebug, logDebug, logDebug, logDebug, logError, logError, logError, logError, logInfo, logInfo, logInfo, logInfo, logName, LogStringContext, logTrace, logTrace, logTrace, logTrace, logWarning, logWarning, logWarning, logWarning, org$apache$spark$internal$Logging$$log_, org$apache$spark$internal$Logging$$log__$eq, withLogContext

  • Method Details

    • csv

      public Dataset<Row> csv(String path)
      Loads a CSV file stream and returns the result as a DataFrame.

      This function will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using schema.

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      You can find the CSV-specific options for reading CSV file stream in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • format

      public DataStreamReader format(String source)
      Specifies the input data source format.

      Parameters:
      source - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • json

      public Dataset<Row> json(String path)
      Loads a JSON file stream and returns the results as a DataFrame.

      JSON Lines (newline-delimited JSON) is supported by default. For JSON (one record per file), set the multiLine option to true.

      This function goes through the input once to determine the input schema. If you know the schema in advance, use the version that specifies the schema to avoid the extra scan.

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      You can find the JSON-specific options for reading JSON file stream in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • load

      public Dataset<Row> load()
      Loads input data stream in as a DataFrame, for data streams that don't require a path (e.g. external key-value stores).

      Returns:
      (undocumented)
      Since:
      2.0.0

    • load

      public Dataset<Row> load(String path)
      Loads input in as a DataFrame, for data streams that read from some path.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • option

      public DataStreamReader option(String key, String value)
      Adds an input option for the underlying data source.

      Parameters:
      key - (undocumented)
      value - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • option

      public DataStreamReader option(String key, boolean value)
      Adds an input option for the underlying data source.

      Parameters:
      key - (undocumented)
      value - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • option

      public DataStreamReader option(String key, long value)
      Adds an input option for the underlying data source.

      Parameters:
      key - (undocumented)
      value - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • option

      public DataStreamReader option(String key, double value)
      Adds an input option for the underlying data source.

      Parameters:
      key - (undocumented)
      value - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • options

      public DataStreamReader options(scala.collection.Map<String,String> options)
      (Scala-specific) Adds input options for the underlying data source.

      Parameters:
      options - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • options

      public DataStreamReader options(Map<String,String> options)
      (Java-specific) Adds input options for the underlying data source.

      Parameters:
      options - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • orc

      public Dataset<Row> orc(String path)
      Loads a ORC file stream, returning the result as a DataFrame.

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      ORC-specific option(s) for reading ORC file stream can be found in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.3.0

    • parquet

      public Dataset<Row> parquet(String path)
      Loads a Parquet file stream, returning the result as a DataFrame.

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      Parquet-specific option(s) for reading Parquet file stream can be found in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • schema

      public DataStreamReader schema(StructType schema)
      Specifies the input schema. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading.

      Parameters:
      schema - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • schema

      public DataStreamReader schema(String schemaString)
      Specifies the schema by using the input DDL-formatted string. Some data sources (e.g. JSON) can infer the input schema automatically from data. By specifying the schema here, the underlying data source can skip the schema inference step, and thus speed up data loading.

      Parameters:
      schemaString - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.3.0

    • table

      public Dataset<Row> table(String tableName)
      Define a Streaming DataFrame on a Table. The DataSource corresponding to the table should support streaming mode.
      Parameters:
      tableName - The name of the table
      Returns:
      (undocumented)
      Since:
      3.1.0

    • text

      public Dataset<Row> text(String path)
      Loads text files and returns a DataFrame whose schema starts with a string column named "value", and followed by partitioned columns if there are any. The text files must be encoded as UTF-8.

      By default, each line in the text files is a new row in the resulting DataFrame. For example: 

      
   // Scala:
   spark.readStream.text("/path/to/directory/")

   // Java:
   spark.readStream().text("/path/to/directory/")

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      You can find the text-specific options for reading text files in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      2.0.0

    • textFile

      public Dataset<String> textFile(String path)
      Loads text file(s) and returns a Dataset of String. The underlying schema of the Dataset contains a single string column named "value". The text files must be encoded as UTF-8.

      If the directory structure of the text files contains partitioning information, those are ignored in the resulting Dataset. To include partitioning information as columns, use text.

      By default, each line in the text file is a new element in the resulting Dataset. For example: 

      
   // Scala:
   spark.readStream.textFile("/path/to/spark/README.md")

   // Java:
   spark.readStream().textFile("/path/to/spark/README.md")

      You can set the text-specific options as specified in DataStreamReader.text.

      Parameters:
      path - input path
      Returns:
      (undocumented)
      Since:
      2.1.0

    • xml

      public Dataset<Row> xml(String path)
      Loads a XML file stream and returns the result as a DataFrame.

      This function will go through the input once to determine the input schema if inferSchema is enabled. To avoid going through the entire data once, disable inferSchema option or specify the schema explicitly using schema.

      You can set the following option(s):

      • maxFilesPerTrigger (default: no max limit): sets the maximum number of new files to be considered in every trigger.
      • maxBytesPerTrigger (default: no max limit): sets the maximum total size of new files to be considered in every trigger.

      You can find the XML-specific options for reading XML file stream in Data Source Option in the version you use.

      Parameters:
      path - (undocumented)
      Returns:
      (undocumented)
      Since:
      4.0.0